Safari Mir Jafar Sadegh
Department of Civil Engineering, Yaşar University, Izmir, Turkey E-mail:
Water Sci Technol. 2019 Mar;79(6):1113-1122. doi: 10.2166/wst.2019.106.
Sediment deposition in sewers and urban drainage systems has great effect on the hydraulic capacity of the channel. In this respect, the self-cleansing concept has been widely used for sewers and urban drainage systems design. This study investigates the bed load sediment transport in sewer pipes with particular reference to the non-deposition condition in clean bed channels. Four data sets available in the literature covering wide ranges of pipe size, sediment size and sediment volumetric concentration have been utilized through applying decision tree (DT), generalized regression neural network (GR) and multivariate adaptive regression splines (MARS) techniques for modeling. The developed models have been compared with conventional regression models available in the literature. The model performance indicators, showed that DT, GR and MARS models outperform conventional regression models. Result shows that GR and MARS models are comparable in terms of calculating particle Froude number and performing better than DT. It is concluded that conventional regression models generally overestimate particle Froude number for the non-deposition condition of sediment transport, while DT, GR and MARS outputs are close to their measured counterparts.
污水和城市排水系统中的沉积物沉积对渠道的水力容量有很大影响。在这方面，自净概念已广泛应用于污水和城市排水系统设计。本研究特别针对清洁河床渠道中的非沉积条件，研究污水管道中的推移质泥沙输移。通过应用决策树（DT）、广义回归神经网络（GR）和多元自适应回归样条（MARS）技术进行建模，利用了文献中涵盖广泛管径、泥沙粒径和泥沙体积浓度范围的四个数据集。已将所开发的模型与文献中可用的传统回归模型进行了比较。模型性能指标表明，DT、GR和MARS模型优于传统回归模型。结果表明，GR和MARS模型在计算颗粒弗劳德数方面具有可比性，且比DT表现更好。得出的结论是，对于泥沙输移的非沉积条件，传统回归模型通常高估颗粒弗劳德数，而DT、GR和MARS的输出结果与实测值接近。
